The present invention is directed to a technique for processing electrocardiagram signals, and more particularly to a technique for diagnosing myocardial ischemia.
The detection and localization of, reversible myocardial ischemia (low heart blood flow which causes reduced oxygen delivery to the heart) of minimal to moderate degree is a major goal in the identification of potential candidates for coronary artery revascularization before ischemic infarction (death of heart muscle) or arrhythmias develop. Although local cardiac effects of various kinds can be captured from epicardial array ECG records, from a clinical and experimental point of view it is highly desirable to have an ECG signal processing methodology that will generate a set of quantifiable ECG variables which can be used for the delineation of ischemic myocardium and the evaluation of its severity.
Unfortunately, present ECG signal analysis for ischemia detection is largely heuristic, too ambiguous to permit a reasonably uniform implementation in computer programs and often not diagnostic prior to myocardial damage. Most ECG processing schemes have adopted a simplified approach for representing ECG waveforms using decision variables based on measurements of the time of ECG wave duration, and the amplitude and slope characteristics. See, for example J. M. Jenkins, Automated Electrocardiography and Arrhythmia Monitoring, Prog. Cardiovas. Dis., vol. 5, p. 367, 1983 (hereinafter reference 1): J. Euderle, M. Telerman, K. Chesky and H. Jaffe, Computer Interpretation of Electrocardiograms and Vectocardiograms, Advances in Cardiology, vol. 16, p. 194, 1976 (reference 2); U. Jain, P. M. Rautaharju and B. M. Horacek, The Stability of Decision Theoretic Electrocardiographic Classifiers Based On the Use of Discretized Features, Comp. Biomed. Res., vol. 13. p. 695, 1980 (reference 3); C. Laxer, R. E. Ideker and T. C. Pilkington, The Use of Unipolar QRS Potentials To Estimate Myocardial Infarction, IEEE Trans. Biomed. Engin., vol. BME-32(1), January 1985 (reference 4); and F. J. Claydon, III, T. C. Pilkington and R. E. Ideker. Classification of Heart Tissue From Bipolar and Unipolar Intramural Potentials, IEEE Trans. Biomed. Engin., vol. BME-32(7). July 1985 (Reference 5). To this end also, many investigators have used various ECG signal mapping procedures for the detection of myocardial ischemia from array ECG signals, either epicardial or torso. The most widely used procedures are isopotential mapping as described by S. Rush and E. Lepeschkin, Body Surface Mapping of Cardiac Field, Advances in Cardiology, vol. 10. 1974. (Reference 6) isochronic mapping as described by D. Durrer, R. T. Van Dam. G. E. Freud, M. J. Janse, F. L. Meijler. and R. C. Arzbaecher, Total Excitation of the Isolated Human Heart. Circulation, vol. 41, pp. 899-912, June 1970 (Reference 7). TQ-ST segment mapping and regional monophasic action potentials and their first derivatives, as described by M. R. Franz, J. T. Flaherty, E. V. Platia, B. H. Bulkley, and M. L. Weisfeldt, Localization of Regional Myocardial Ischemia By Recording of Monophasic Action Potentials Circulation, vol 69, pp. 593-604, March 1984 (Reference 8). However, these approaches are fundamentally simple and to some respect primitive methods that do not take into account the probabilistic nature associated with decision making procedures. At best, mapping procedures are characterized by a small number of salient features such as the number and location of potential extrema, as described by F. A. Roberge, Some Emerging Issues In Electrocardiology, Proc. Special Symp. on Critic Emerg. Issues in Biom. Engin., C. J. Robinson and G. V. Kondraske (editors), pp. 59-61, IEEE Press No. 86CH2369-7, November 1986 (Reference 9). They are not easily interpreted as a whole and do not effectively use all of the experimentally measured information. Finally since all the aforementioned schemes operate in the time-domain of the data they are vulnerable to measurement errors in baseline detection, and locating onsets and offsets of ECG waves in the presence of noise. This is described by F. Kornreich, P. M. Rautaharju, J. W. Warren, B. M. Horacek, and M. Dramaix, Effective Extraction of Diagnostic ECG Waveform Information Using Orthonormal Basis Functions Derived From Body Surface Potential Maps, Journ. Electrocardiol., vol. 18(4), pp. 341-350, 1985 (Reference 10). There has also been ample evidence concerning the weak robustness of these approaches, as described for example by R. R. Helppi, V. Unite, and H. K. Wolf, Suggested Minimal Performance Requirements and Methods of Performance Evaluations For Computer ECG Analysis programs CMA Journ., vol. 108, p. 1251, 1973 (Reference 11).
The frequency content of ECG signals has been used to detect myocardial pathology as an alternative approach to time domain techniques. This has been described by T. L. Nichols, and D. M. Mirvis, Frequency Content of the Electrocardiogram. Spatial Features And Effects of Myocardial Infarctions. Journ. Electrocardiology, vol. 18(2). pp. 185-194, 1985 (Reference 12). In this study of array ECG signals, the spatial variation in the frequency content of the electrocardiogram was mapped and found to be affected by myocardial infarction. However, no decision variables were identified which permit quantitative localization or probabilistic decision-making.
The detection of non-infarcting reversible ventricular ischemia, but not its localization, has been done by application of an advanced signal processing methodology which deals with an ECG array as a multidimensional power frequency spectrum. See, for example, C. L. Nikias, M. R. Raghuveer, J. H. Siegel and M. Fabian. The Zero-delay Wavenumber Spectrum Estimation For the Analysis of Array ECG Signals - An Alternative to Isopotential Mapping, IEEE Trans. Biomed. Engin vol. BME-33(4), pp. 435-452, April 1986 (Reference 13); J. H. Siegel, C. L. Nikias, M. R. Raghuveer, M. Fabian, K. C. Goh and D. Sanford, Epicardial Electrical Activation Analyzed Via Frequency-Wavenumber Spectrum Estimation For the Characterization of Arrhythmiagenic States, Journ. Electrocardiology (to be published 1987) (Reference 14); C. L. Nikias and J. H. Siegel, Knowledge Based Classification of Epicardial Electric Activation From Array ECG Signals, Proc. 12th Northeast Bioeng. Conf., pp. 198-200, Yale University, March 1986 (Reference 15); and C. L. Nikias. J. H. Siegel, M. R. Raghuveer and M. Fabian. Spectrum Estimation For the Analysis of Array ECG. Proc. 7th Annual Int. Conf. IEEE Engin. in Medicine and Biology pp. 824-829, Chicago, Ill., September 1985 (Reference 16). This method allows for the computation and compact graphic display of frequency-wavenumber spectrum estimates (FWSE) of array ECG signals by the use of a Zero-Delay Wavenumber Spectrum Estimation (ZDWS) technique (e.g., see References 13 and 14). It has been demonstrated that the ZDWS method provides a means for objective quantification of the alterations in cardiac activation produced by areas of myocardial ischemia because key parameters associated with abnormalities in electrical wavefront propagation are easily revealed in the spectrum domain by analysis using the form of a hierarchical tree structure (e.g., see Reference 15). The ZDWS method does not require uniformly spaced sensors and can accommodate any shape of ECG array. Successful experimental and clinical use of the FWSE method to the analysis of epicardial as well as human body surface array ECG signals has been presented (e.g., see References 13, 14, & 16). Its application to the inverse problem of electrocardiography also has been shown, e.g., as described by C. L. Nikias, T. Y. Shen and J. H. Siegel Frequency-Wavenumber Invers Model For Electrocardiography, Proc. 8th Annual Int. Conf. IEEE Engin. in Medicine and Biology, pp. 307-310, Dallas-Fort Worth, Tex., November 1986 (Reference 17).
However, while specific for detection of ischemic modulation of ventricular activation characteristics, the multidimensional nature of the ZDWS technique does not permit localization of ischemic areas, nor does it allow for a direct comparison of ischemia quantification with biochemical parameters of the ischemic myocardium as related to specific sensor locations. Moreover, none of the previously mentioned methods permit the use of probability analysis statistics of the Bayesian type to quantify the likelihood of correct detection of a given area as ischemic, compared to its probability of being normal.
Accordingly, it is an object of the present invention to develop an advanced ECG signal processing methodology which could be directly used to detect, localize and quantify the level of biochemical severity of areas of ischemic myocardium.